Project1
2023-02-20
library(titanic)
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library(mice)##
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## cbind, rbindproductfailure = read_csv("train.csv")## Rows: 26570 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): product_code, attribute_0, attribute_1, failure
## dbl (22): id, loading, attribute_2, attribute_3, measurement_0, measurement_...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(productfailure)## spc_tbl_ [26,570 × 26] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : num [1:26570] 0 1 2 3 4 5 6 7 8 9 ...
## $ product_code : chr [1:26570] "A" "A" "A" "A" ...
## $ loading : num [1:26570] 80.1 84.9 82.4 101.1 188.1 ...
## $ attribute_0 : chr [1:26570] "material_7" "material_7" "material_7" "material_7" ...
## $ attribute_1 : chr [1:26570] "material_8" "material_8" "material_8" "material_8" ...
## $ attribute_2 : num [1:26570] 9 9 9 9 9 9 9 9 9 9 ...
## $ attribute_3 : num [1:26570] 5 5 5 5 5 5 5 5 5 5 ...
## $ measurement_0 : num [1:26570] 7 14 12 13 9 11 12 4 9 10 ...
## $ measurement_1 : num [1:26570] 8 3 1 2 2 4 2 8 6 4 ...
## $ measurement_2 : num [1:26570] 4 3 5 6 8 0 4 8 5 7 ...
## $ measurement_3 : num [1:26570] 18 18.2 18.1 17.3 19.3 ...
## $ measurement_4 : num [1:26570] 12.5 11.5 11.7 11.2 12.9 ...
## $ measurement_5 : num [1:26570] 15.7 17.7 16.7 18.6 17 ...
## $ measurement_6 : num [1:26570] 19.3 17.9 18.2 18.3 15.7 ...
## $ measurement_7 : num [1:26570] 11.7 12.7 12.7 12.6 11.3 ...
## $ measurement_8 : num [1:26570] 20.2 17.9 18.3 19.1 18.1 ...
## $ measurement_9 : num [1:26570] 10.7 12.4 12.7 12.5 10.3 ...
## $ measurement_10: num [1:26570] 15.9 17.9 15.6 16.3 17.1 ...
## $ measurement_11: num [1:26570] 17.6 17.9 NA 18.4 19.9 ...
## $ measurement_12: num [1:26570] 15.2 11.8 13.8 10 12.4 ...
## $ measurement_13: num [1:26570] 15 14.7 16.7 15.2 16.2 ...
## $ measurement_14: num [1:26570] NA 15.4 18.6 15.6 12.8 ...
## $ measurement_15: num [1:26570] 13 14.4 14.1 16.2 13.2 ...
## $ measurement_16: num [1:26570] 14.7 15.6 17.9 17.2 16.4 ...
## $ measurement_17: num [1:26570] 764 682 663 826 580 ...
## $ failure : chr [1:26570] "No" "No" "No" "No" ...
## - attr(*, "spec")=
## .. cols(
## .. id = col_double(),
## .. product_code = col_character(),
## .. loading = col_double(),
## .. attribute_0 = col_character(),
## .. attribute_1 = col_character(),
## .. attribute_2 = col_double(),
## .. attribute_3 = col_double(),
## .. measurement_0 = col_double(),
## .. measurement_1 = col_double(),
## .. measurement_2 = col_double(),
## .. measurement_3 = col_double(),
## .. measurement_4 = col_double(),
## .. measurement_5 = col_double(),
## .. measurement_6 = col_double(),
## .. measurement_7 = col_double(),
## .. measurement_8 = col_double(),
## .. measurement_9 = col_double(),
## .. measurement_10 = col_double(),
## .. measurement_11 = col_double(),
## .. measurement_12 = col_double(),
## .. measurement_13 = col_double(),
## .. measurement_14 = col_double(),
## .. measurement_15 = col_double(),
## .. measurement_16 = col_double(),
## .. measurement_17 = col_double(),
## .. failure = col_character()
## .. )
## - attr(*, "problems")=<externalptr>summary(productfailure)## id product_code loading attribute_0
## Min. : 0 Length:26570 Min. : 33.16 Length:26570
## 1st Qu.: 6642 Class :character 1st Qu.: 99.99 Class :character
## Median :13284 Mode :character Median :122.39 Mode :character
## Mean :13284 Mean :127.83
## 3rd Qu.:19927 3rd Qu.:149.15
## Max. :26569 Max. :385.86
## NA's :250
## attribute_1 attribute_2 attribute_3 measurement_0
## Length:26570 Min. :5.000 Min. :5.00 Min. : 0.000
## Class :character 1st Qu.:6.000 1st Qu.:6.00 1st Qu.: 4.000
## Mode :character Median :6.000 Median :8.00 Median : 7.000
## Mean :6.754 Mean :7.24 Mean : 7.416
## 3rd Qu.:8.000 3rd Qu.:8.00 3rd Qu.:10.000
## Max. :9.000 Max. :9.00 Max. :29.000
##
## measurement_1 measurement_2 measurement_3 measurement_4
## Min. : 0.000 Min. : 0.000 Min. :13.97 Min. : 8.008
## 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.:17.12 1st Qu.:11.051
## Median : 8.000 Median : 6.000 Median :17.79 Median :11.733
## Mean : 8.233 Mean : 6.257 Mean :17.79 Mean :11.732
## 3rd Qu.:11.000 3rd Qu.: 8.000 3rd Qu.:18.47 3rd Qu.:12.410
## Max. :29.000 Max. :24.000 Max. :21.50 Max. :16.484
## NA's :381 NA's :538
## measurement_5 measurement_6 measurement_7 measurement_8
## Min. :12.07 Min. :12.71 Min. : 7.968 Min. :15.22
## 1st Qu.:16.44 1st Qu.:16.84 1st Qu.:11.045 1st Qu.:18.34
## Median :17.13 Median :17.52 Median :11.712 Median :19.02
## Mean :17.13 Mean :17.51 Mean :11.717 Mean :19.02
## 3rd Qu.:17.80 3rd Qu.:18.18 3rd Qu.:12.391 3rd Qu.:19.71
## Max. :21.43 Max. :21.54 Max. :15.419 Max. :23.81
## NA's :676 NA's :796 NA's :937 NA's :1048
## measurement_9 measurement_10 measurement_11 measurement_12
## Min. : 7.537 Min. : 9.323 Min. :12.46 Min. : 5.167
## 1st Qu.:10.757 1st Qu.:15.209 1st Qu.:18.17 1st Qu.:10.703
## Median :11.430 Median :16.127 Median :19.21 Median :11.717
## Mean :11.431 Mean :16.118 Mean :19.17 Mean :11.703
## 3rd Qu.:12.102 3rd Qu.:17.025 3rd Qu.:20.21 3rd Qu.:12.709
## Max. :15.412 Max. :22.479 Max. :25.64 Max. :17.663
## NA's :1227 NA's :1300 NA's :1468 NA's :1601
## measurement_13 measurement_14 measurement_15 measurement_16
## Min. :10.89 Min. : 9.14 Min. : 9.104 Min. : 9.701
## 1st Qu.:14.89 1st Qu.:15.06 1st Qu.:13.957 1st Qu.:15.268
## Median :15.63 Median :16.04 Median :14.969 Median :16.436
## Mean :15.65 Mean :16.05 Mean :14.996 Mean :16.461
## 3rd Qu.:16.37 3rd Qu.:17.08 3rd Qu.:16.018 3rd Qu.:17.628
## Max. :22.71 Max. :22.30 Max. :21.626 Max. :24.094
## NA's :1774 NA's :1874 NA's :2009 NA's :2110
## measurement_17 failure
## Min. : 196.8 Length:26570
## 1st Qu.: 619.0 Class :character
## Median : 701.0 Mode :character
## Mean : 701.3
## 3rd Qu.: 784.1
## Max. :1312.8
## NA's :2284productfailure ## # A tibble: 26,570 × 26
## id produ…¹ loading attri…² attri…³ attri…⁴ attri…⁵ measu…⁶ measu…⁷ measu…⁸
## <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 A 80.1 materi… materi… 9 5 7 8 4
## 2 1 A 84.9 materi… materi… 9 5 14 3 3
## 3 2 A 82.4 materi… materi… 9 5 12 1 5
## 4 3 A 101. materi… materi… 9 5 13 2 6
## 5 4 A 188. materi… materi… 9 5 9 2 8
## 6 5 A 75.4 materi… materi… 9 5 11 4 0
## 7 6 A 162. materi… materi… 9 5 12 2 4
## 8 7 A 178. materi… materi… 9 5 4 8 8
## 9 8 A 110. materi… materi… 9 5 9 6 5
## 10 9 A 98.7 materi… materi… 9 5 10 4 7
## # … with 26,560 more rows, 16 more variables: measurement_3 <dbl>,
## # measurement_4 <dbl>, measurement_5 <dbl>, measurement_6 <dbl>,
## # measurement_7 <dbl>, measurement_8 <dbl>, measurement_9 <dbl>,
## # measurement_10 <dbl>, measurement_11 <dbl>, measurement_12 <dbl>,
## # measurement_13 <dbl>, measurement_14 <dbl>, measurement_15 <dbl>,
## # measurement_16 <dbl>, measurement_17 <dbl>, failure <chr>, and abbreviated
## # variable names ¹product_code, ²attribute_0, ³attribute_1, ⁴attribute_2, …productfailure = productfailure %>% mutate(failure = as_factor(failure)) %>%
mutate(product_code = as_factor(product_code)) %>% mutate(attribute_0 = as_factor(attribute_0)) %>%
mutate(attribute_1 = as_factor(attribute_1))
summary(productfailure)## id product_code loading attribute_0
## Min. : 0 A:5100 Min. : 33.16 material_7:21320
## 1st Qu.: 6642 B:5250 1st Qu.: 99.99 material_5: 5250
## Median :13284 C:5765 Median :122.39
## Mean :13284 D:5112 Mean :127.83
## 3rd Qu.:19927 E:5343 3rd Qu.:149.15
## Max. :26569 Max. :385.86
## NA's :250
## attribute_1 attribute_2 attribute_3 measurement_0
## material_8:10865 Min. :5.000 Min. :5.00 Min. : 0.000
## material_5:10362 1st Qu.:6.000 1st Qu.:6.00 1st Qu.: 4.000
## material_6: 5343 Median :6.000 Median :8.00 Median : 7.000
## Mean :6.754 Mean :7.24 Mean : 7.416
## 3rd Qu.:8.000 3rd Qu.:8.00 3rd Qu.:10.000
## Max. :9.000 Max. :9.00 Max. :29.000
##
## measurement_1 measurement_2 measurement_3 measurement_4
## Min. : 0.000 Min. : 0.000 Min. :13.97 Min. : 8.008
## 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.:17.12 1st Qu.:11.051
## Median : 8.000 Median : 6.000 Median :17.79 Median :11.733
## Mean : 8.233 Mean : 6.257 Mean :17.79 Mean :11.732
## 3rd Qu.:11.000 3rd Qu.: 8.000 3rd Qu.:18.47 3rd Qu.:12.410
## Max. :29.000 Max. :24.000 Max. :21.50 Max. :16.484
## NA's :381 NA's :538
## measurement_5 measurement_6 measurement_7 measurement_8
## Min. :12.07 Min. :12.71 Min. : 7.968 Min. :15.22
## 1st Qu.:16.44 1st Qu.:16.84 1st Qu.:11.045 1st Qu.:18.34
## Median :17.13 Median :17.52 Median :11.712 Median :19.02
## Mean :17.13 Mean :17.51 Mean :11.717 Mean :19.02
## 3rd Qu.:17.80 3rd Qu.:18.18 3rd Qu.:12.391 3rd Qu.:19.71
## Max. :21.43 Max. :21.54 Max. :15.419 Max. :23.81
## NA's :676 NA's :796 NA's :937 NA's :1048
## measurement_9 measurement_10 measurement_11 measurement_12
## Min. : 7.537 Min. : 9.323 Min. :12.46 Min. : 5.167
## 1st Qu.:10.757 1st Qu.:15.209 1st Qu.:18.17 1st Qu.:10.703
## Median :11.430 Median :16.127 Median :19.21 Median :11.717
## Mean :11.431 Mean :16.118 Mean :19.17 Mean :11.703
## 3rd Qu.:12.102 3rd Qu.:17.025 3rd Qu.:20.21 3rd Qu.:12.709
## Max. :15.412 Max. :22.479 Max. :25.64 Max. :17.663
## NA's :1227 NA's :1300 NA's :1468 NA's :1601
## measurement_13 measurement_14 measurement_15 measurement_16
## Min. :10.89 Min. : 9.14 Min. : 9.104 Min. : 9.701
## 1st Qu.:14.89 1st Qu.:15.06 1st Qu.:13.957 1st Qu.:15.268
## Median :15.63 Median :16.04 Median :14.969 Median :16.436
## Mean :15.65 Mean :16.05 Mean :14.996 Mean :16.461
## 3rd Qu.:16.37 3rd Qu.:17.08 3rd Qu.:16.018 3rd Qu.:17.628
## Max. :22.71 Max. :22.30 Max. :21.626 Max. :24.094
## NA's :1774 NA's :1874 NA's :2009 NA's :2110
## measurement_17 failure
## Min. : 196.8 No :20921
## 1st Qu.: 619.0 Yes: 5649
## Median : 701.0
## Mean : 701.3
## 3rd Qu.: 784.1
## Max. :1312.8
## NA's :2284str(productfailure)## tibble [26,570 × 26] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:26570] 0 1 2 3 4 5 6 7 8 9 ...
## $ product_code : Factor w/ 5 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ loading : num [1:26570] 80.1 84.9 82.4 101.1 188.1 ...
## $ attribute_0 : Factor w/ 2 levels "material_7","material_5": 1 1 1 1 1 1 1 1 1 1 ...
## $ attribute_1 : Factor w/ 3 levels "material_8","material_5",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ attribute_2 : num [1:26570] 9 9 9 9 9 9 9 9 9 9 ...
## $ attribute_3 : num [1:26570] 5 5 5 5 5 5 5 5 5 5 ...
## $ measurement_0 : num [1:26570] 7 14 12 13 9 11 12 4 9 10 ...
## $ measurement_1 : num [1:26570] 8 3 1 2 2 4 2 8 6 4 ...
## $ measurement_2 : num [1:26570] 4 3 5 6 8 0 4 8 5 7 ...
## $ measurement_3 : num [1:26570] 18 18.2 18.1 17.3 19.3 ...
## $ measurement_4 : num [1:26570] 12.5 11.5 11.7 11.2 12.9 ...
## $ measurement_5 : num [1:26570] 15.7 17.7 16.7 18.6 17 ...
## $ measurement_6 : num [1:26570] 19.3 17.9 18.2 18.3 15.7 ...
## $ measurement_7 : num [1:26570] 11.7 12.7 12.7 12.6 11.3 ...
## $ measurement_8 : num [1:26570] 20.2 17.9 18.3 19.1 18.1 ...
## $ measurement_9 : num [1:26570] 10.7 12.4 12.7 12.5 10.3 ...
## $ measurement_10: num [1:26570] 15.9 17.9 15.6 16.3 17.1 ...
## $ measurement_11: num [1:26570] 17.6 17.9 NA 18.4 19.9 ...
## $ measurement_12: num [1:26570] 15.2 11.8 13.8 10 12.4 ...
## $ measurement_13: num [1:26570] 15 14.7 16.7 15.2 16.2 ...
## $ measurement_14: num [1:26570] NA 15.4 18.6 15.6 12.8 ...
## $ measurement_15: num [1:26570] 13 14.4 14.1 16.2 13.2 ...
## $ measurement_16: num [1:26570] 14.7 15.6 17.9 17.2 16.4 ...
## $ measurement_17: num [1:26570] 764 682 663 826 580 ...
## $ failure : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 2 1 1 ...summary(productfailure)## id product_code loading attribute_0
## Min. : 0 A:5100 Min. : 33.16 material_7:21320
## 1st Qu.: 6642 B:5250 1st Qu.: 99.99 material_5: 5250
## Median :13284 C:5765 Median :122.39
## Mean :13284 D:5112 Mean :127.83
## 3rd Qu.:19927 E:5343 3rd Qu.:149.15
## Max. :26569 Max. :385.86
## NA's :250
## attribute_1 attribute_2 attribute_3 measurement_0
## material_8:10865 Min. :5.000 Min. :5.00 Min. : 0.000
## material_5:10362 1st Qu.:6.000 1st Qu.:6.00 1st Qu.: 4.000
## material_6: 5343 Median :6.000 Median :8.00 Median : 7.000
## Mean :6.754 Mean :7.24 Mean : 7.416
## 3rd Qu.:8.000 3rd Qu.:8.00 3rd Qu.:10.000
## Max. :9.000 Max. :9.00 Max. :29.000
##
## measurement_1 measurement_2 measurement_3 measurement_4
## Min. : 0.000 Min. : 0.000 Min. :13.97 Min. : 8.008
## 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.:17.12 1st Qu.:11.051
## Median : 8.000 Median : 6.000 Median :17.79 Median :11.733
## Mean : 8.233 Mean : 6.257 Mean :17.79 Mean :11.732
## 3rd Qu.:11.000 3rd Qu.: 8.000 3rd Qu.:18.47 3rd Qu.:12.410
## Max. :29.000 Max. :24.000 Max. :21.50 Max. :16.484
## NA's :381 NA's :538
## measurement_5 measurement_6 measurement_7 measurement_8
## Min. :12.07 Min. :12.71 Min. : 7.968 Min. :15.22
## 1st Qu.:16.44 1st Qu.:16.84 1st Qu.:11.045 1st Qu.:18.34
## Median :17.13 Median :17.52 Median :11.712 Median :19.02
## Mean :17.13 Mean :17.51 Mean :11.717 Mean :19.02
## 3rd Qu.:17.80 3rd Qu.:18.18 3rd Qu.:12.391 3rd Qu.:19.71
## Max. :21.43 Max. :21.54 Max. :15.419 Max. :23.81
## NA's :676 NA's :796 NA's :937 NA's :1048
## measurement_9 measurement_10 measurement_11 measurement_12
## Min. : 7.537 Min. : 9.323 Min. :12.46 Min. : 5.167
## 1st Qu.:10.757 1st Qu.:15.209 1st Qu.:18.17 1st Qu.:10.703
## Median :11.430 Median :16.127 Median :19.21 Median :11.717
## Mean :11.431 Mean :16.118 Mean :19.17 Mean :11.703
## 3rd Qu.:12.102 3rd Qu.:17.025 3rd Qu.:20.21 3rd Qu.:12.709
## Max. :15.412 Max. :22.479 Max. :25.64 Max. :17.663
## NA's :1227 NA's :1300 NA's :1468 NA's :1601
## measurement_13 measurement_14 measurement_15 measurement_16
## Min. :10.89 Min. : 9.14 Min. : 9.104 Min. : 9.701
## 1st Qu.:14.89 1st Qu.:15.06 1st Qu.:13.957 1st Qu.:15.268
## Median :15.63 Median :16.04 Median :14.969 Median :16.436
## Mean :15.65 Mean :16.05 Mean :14.996 Mean :16.461
## 3rd Qu.:16.37 3rd Qu.:17.08 3rd Qu.:16.018 3rd Qu.:17.628
## Max. :22.71 Max. :22.30 Max. :21.626 Max. :24.094
## NA's :1774 NA's :1874 NA's :2009 NA's :2110
## measurement_17 failure
## Min. : 196.8 No :20921
## 1st Qu.: 619.0 Yes: 5649
## Median : 701.0
## Mean : 701.3
## 3rd Qu.: 784.1
## Max. :1312.8
## NA's :2284str(productfailure)## tibble [26,570 × 26] (S3: tbl_df/tbl/data.frame)
## $ id : num [1:26570] 0 1 2 3 4 5 6 7 8 9 ...
## $ product_code : Factor w/ 5 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ loading : num [1:26570] 80.1 84.9 82.4 101.1 188.1 ...
## $ attribute_0 : Factor w/ 2 levels "material_7","material_5": 1 1 1 1 1 1 1 1 1 1 ...
## $ attribute_1 : Factor w/ 3 levels "material_8","material_5",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ attribute_2 : num [1:26570] 9 9 9 9 9 9 9 9 9 9 ...
## $ attribute_3 : num [1:26570] 5 5 5 5 5 5 5 5 5 5 ...
## $ measurement_0 : num [1:26570] 7 14 12 13 9 11 12 4 9 10 ...
## $ measurement_1 : num [1:26570] 8 3 1 2 2 4 2 8 6 4 ...
## $ measurement_2 : num [1:26570] 4 3 5 6 8 0 4 8 5 7 ...
## $ measurement_3 : num [1:26570] 18 18.2 18.1 17.3 19.3 ...
## $ measurement_4 : num [1:26570] 12.5 11.5 11.7 11.2 12.9 ...
## $ measurement_5 : num [1:26570] 15.7 17.7 16.7 18.6 17 ...
## $ measurement_6 : num [1:26570] 19.3 17.9 18.2 18.3 15.7 ...
## $ measurement_7 : num [1:26570] 11.7 12.7 12.7 12.6 11.3 ...
## $ measurement_8 : num [1:26570] 20.2 17.9 18.3 19.1 18.1 ...
## $ measurement_9 : num [1:26570] 10.7 12.4 12.7 12.5 10.3 ...
## $ measurement_10: num [1:26570] 15.9 17.9 15.6 16.3 17.1 ...
## $ measurement_11: num [1:26570] 17.6 17.9 NA 18.4 19.9 ...
## $ measurement_12: num [1:26570] 15.2 11.8 13.8 10 12.4 ...
## $ measurement_13: num [1:26570] 15 14.7 16.7 15.2 16.2 ...
## $ measurement_14: num [1:26570] NA 15.4 18.6 15.6 12.8 ...
## $ measurement_15: num [1:26570] 13 14.4 14.1 16.2 13.2 ...
## $ measurement_16: num [1:26570] 14.7 15.6 17.9 17.2 16.4 ...
## $ measurement_17: num [1:26570] 764 682 663 826 580 ...
## $ failure : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 2 1 1 ...skim(productfailure)| Name | productfailure |
| Number of rows | 26570 |
| Number of columns | 26 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 22 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| product_code | 0 | 1 | FALSE | 5 | C: 5765, E: 5343, B: 5250, D: 5112 |
| attribute_0 | 0 | 1 | FALSE | 2 | mat: 21320, mat: 5250 |
| attribute_1 | 0 | 1 | FALSE | 3 | mat: 10865, mat: 10362, mat: 5343 |
| failure | 0 | 1 | FALSE | 2 | No: 20921, Yes: 5649 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1.00 | 13284.50 | 7670.24 | 0.00 | 6642.25 | 13284.50 | 19926.75 | 26569.00 | ▇▇▇▇▇ |
| loading | 250 | 0.99 | 127.83 | 39.03 | 33.16 | 99.99 | 122.39 | 149.15 | 385.86 | ▃▇▂▁▁ |
| attribute_2 | 0 | 1.00 | 6.75 | 1.47 | 5.00 | 6.00 | 6.00 | 8.00 | 9.00 | ▅▇▁▅▃ |
| attribute_3 | 0 | 1.00 | 7.24 | 1.46 | 5.00 | 6.00 | 8.00 | 8.00 | 9.00 | ▃▃▁▇▃ |
| measurement_0 | 0 | 1.00 | 7.42 | 4.12 | 0.00 | 4.00 | 7.00 | 10.00 | 29.00 | ▆▇▂▁▁ |
| measurement_1 | 0 | 1.00 | 8.23 | 4.20 | 0.00 | 5.00 | 8.00 | 11.00 | 29.00 | ▅▇▃▁▁ |
| measurement_2 | 0 | 1.00 | 6.26 | 3.31 | 0.00 | 4.00 | 6.00 | 8.00 | 24.00 | ▅▇▂▁▁ |
| measurement_3 | 381 | 0.99 | 17.79 | 1.00 | 13.97 | 17.12 | 17.79 | 18.47 | 21.50 | ▁▃▇▃▁ |
| measurement_4 | 538 | 0.98 | 11.73 | 1.00 | 8.01 | 11.05 | 11.73 | 12.41 | 16.48 | ▁▅▇▁▁ |
| measurement_5 | 676 | 0.97 | 17.13 | 1.00 | 12.07 | 16.44 | 17.13 | 17.80 | 21.42 | ▁▁▇▃▁ |
| measurement_6 | 796 | 0.97 | 17.51 | 1.00 | 12.71 | 16.84 | 17.52 | 18.18 | 21.54 | ▁▂▇▅▁ |
| measurement_7 | 937 | 0.96 | 11.72 | 1.00 | 7.97 | 11.04 | 11.71 | 12.39 | 15.42 | ▁▃▇▃▁ |
| measurement_8 | 1048 | 0.96 | 19.02 | 1.01 | 15.22 | 18.34 | 19.02 | 19.71 | 23.81 | ▁▅▇▂▁ |
| measurement_9 | 1227 | 0.95 | 11.43 | 1.00 | 7.54 | 10.76 | 11.43 | 12.10 | 15.41 | ▁▃▇▃▁ |
| measurement_10 | 1300 | 0.95 | 16.12 | 1.41 | 9.32 | 15.21 | 16.13 | 17.02 | 22.48 | ▁▂▇▂▁ |
| measurement_11 | 1468 | 0.94 | 19.17 | 1.52 | 12.46 | 18.17 | 19.21 | 20.21 | 25.64 | ▁▂▇▃▁ |
| measurement_12 | 1601 | 0.94 | 11.70 | 1.49 | 5.17 | 10.70 | 11.72 | 12.71 | 17.66 | ▁▂▇▃▁ |
| measurement_13 | 1774 | 0.93 | 15.65 | 1.16 | 10.89 | 14.89 | 15.63 | 16.37 | 22.71 | ▁▇▇▁▁ |
| measurement_14 | 1874 | 0.93 | 16.05 | 1.49 | 9.14 | 15.06 | 16.04 | 17.08 | 22.30 | ▁▂▇▃▁ |
| measurement_15 | 2009 | 0.92 | 15.00 | 1.55 | 9.10 | 13.96 | 14.97 | 16.02 | 21.63 | ▁▃▇▂▁ |
| measurement_16 | 2110 | 0.92 | 16.46 | 1.71 | 9.70 | 15.27 | 16.44 | 17.63 | 24.09 | ▁▃▇▂▁ |
| measurement_17 | 2284 | 0.91 | 701.27 | 123.30 | 196.79 | 618.96 | 701.02 | 784.09 | 1312.79 | ▁▅▇▁▁ |
productfailure = productfailure %>% dplyr::select(c("failure","product_code","loading","attribute_0","attribute_1","attribute_2","attribute_3","measurement_0","measurement_1","measurement_2","measurement_3","measurement_4","measurement_5","measurement_6","measurement_7","measurement_8","measurement_9","measurement_10","measurement_11","measurement_12","measurement_13","measurement_14","measurement_15","measurement_16","measurement_17"))
set.seed(1234)
imp_prod = mice(productfailure, m=5, method = "pmm", printFlag = FALSE)## Warning: Number of logged events: 768summary(imp_prod)## Class: mids
## Number of multiple imputations: 5
## Imputation methods:
## failure product_code loading attribute_0 attribute_1
## "" "" "pmm" "" ""
## attribute_2 attribute_3 measurement_0 measurement_1 measurement_2
## "" "" "" "" ""
## measurement_3 measurement_4 measurement_5 measurement_6 measurement_7
## "pmm" "pmm" "pmm" "pmm" "pmm"
## measurement_8 measurement_9 measurement_10 measurement_11 measurement_12
## "pmm" "pmm" "pmm" "pmm" "pmm"
## measurement_13 measurement_14 measurement_15 measurement_16 measurement_17
## "pmm" "pmm" "pmm" "pmm" "pmm"
## PredictorMatrix:
## failure product_code loading attribute_0 attribute_1 attribute_2
## failure 0 1 1 1 1 1
## product_code 1 0 1 1 1 1
## loading 1 1 0 1 1 1
## attribute_0 1 1 1 0 1 1
## attribute_1 1 1 1 1 0 1
## attribute_2 1 1 1 1 1 0
## attribute_3 measurement_0 measurement_1 measurement_2
## failure 1 1 1 1
## product_code 1 1 1 1
## loading 1 1 1 1
## attribute_0 1 1 1 1
## attribute_1 1 1 1 1
## attribute_2 1 1 1 1
## measurement_3 measurement_4 measurement_5 measurement_6
## failure 1 1 1 1
## product_code 1 1 1 1
## loading 1 1 1 1
## attribute_0 1 1 1 1
## attribute_1 1 1 1 1
## attribute_2 1 1 1 1
## measurement_7 measurement_8 measurement_9 measurement_10
## failure 1 1 1 1
## product_code 1 1 1 1
## loading 1 1 1 1
## attribute_0 1 1 1 1
## attribute_1 1 1 1 1
## attribute_2 1 1 1 1
## measurement_11 measurement_12 measurement_13 measurement_14
## failure 1 1 1 1
## product_code 1 1 1 1
## loading 1 1 1 1
## attribute_0 1 1 1 1
## attribute_1 1 1 1 1
## attribute_2 1 1 1 1
## measurement_15 measurement_16 measurement_17
## failure 1 1 1
## product_code 1 1 1
## loading 1 1 1
## attribute_0 1 1 1
## attribute_1 1 1 1
## attribute_2 1 1 1
## Number of logged events: 768
## it im dep meth
## 1 1 1 loading pmm
## 2 1 1 loading pmm
## 3 1 1 measurement_3 pmm
## 4 1 1 measurement_3 pmm
## 5 1 1 measurement_4 pmm
## 6 1 1 measurement_4 pmm
## out
## 1 product_codeC, attribute_0material_5, attribute_1material_6, attribute_3, measurement_1
## 2 mice detected that your data are (nearly) multi-collinear.\nIt applied a ridge penalty to continue calculations, but the results can be unstable.\nDoes your dataset contain duplicates, linear transformation, or factors with unique respondent names?
## 3 product_codeB, product_codeE, attribute_1material_5, measurement_0, measurement_2
## 4 mice detected that your data are (nearly) multi-collinear.\nIt applied a ridge penalty to continue calculations, but the results can be unstable.\nDoes your dataset contain duplicates, linear transformation, or factors with unique respondent names?
## 5 product_codeB, product_codeC, attribute_0material_5, attribute_1material_5, measurement_0
## 6 mice detected that your data are (nearly) multi-collinear.\nIt applied a ridge penalty to continue calculations, but the results can be unstable.\nDoes your dataset contain duplicates, linear transformation, or factors with unique respondent names?densityplot(imp_prod, ~measurement_4)
product_failure_complete = complete(imp_prod)
summary(product_failure_complete)## failure product_code loading attribute_0
## No :20921 A:5100 Min. : 33.16 material_7:21320
## Yes: 5649 B:5250 1st Qu.:100.02 material_5: 5250
## C:5765 Median :122.46
## D:5112 Mean :128.01
## E:5343 3rd Qu.:149.32
## Max. :385.86
## attribute_1 attribute_2 attribute_3 measurement_0
## material_8:10865 Min. :5.000 Min. :5.00 Min. : 0.000
## material_5:10362 1st Qu.:6.000 1st Qu.:6.00 1st Qu.: 4.000
## material_6: 5343 Median :6.000 Median :8.00 Median : 7.000
## Mean :6.754 Mean :7.24 Mean : 7.416
## 3rd Qu.:8.000 3rd Qu.:8.00 3rd Qu.:10.000
## Max. :9.000 Max. :9.00 Max. :29.000
## measurement_1 measurement_2 measurement_3 measurement_4
## Min. : 0.000 Min. : 0.000 Min. :13.97 Min. : 8.008
## 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.:17.11 1st Qu.:11.054
## Median : 8.000 Median : 6.000 Median :17.78 Median :11.735
## Mean : 8.233 Mean : 6.257 Mean :17.79 Mean :11.734
## 3rd Qu.:11.000 3rd Qu.: 8.000 3rd Qu.:18.46 3rd Qu.:12.411
## Max. :29.000 Max. :24.000 Max. :21.50 Max. :16.484
## measurement_5 measurement_6 measurement_7 measurement_8
## Min. :12.07 Min. :12.71 Min. : 7.968 Min. :15.22
## 1st Qu.:16.39 1st Qu.:16.86 1st Qu.:10.978 1st Qu.:18.27
## Median :17.10 Median :17.52 Median :11.686 Median :18.97
## Mean :17.09 Mean :17.54 Mean :11.643 Mean :18.96
## 3rd Qu.:17.79 3rd Qu.:18.19 3rd Qu.:12.364 3rd Qu.:19.68
## Max. :21.43 Max. :21.54 Max. :15.419 Max. :23.81
## measurement_9 measurement_10 measurement_11 measurement_12
## Min. : 7.537 Min. : 9.323 Min. :12.46 Min. : 5.167
## 1st Qu.:10.738 1st Qu.:15.219 1st Qu.:18.12 1st Qu.:10.534
## Median :11.445 Median :16.182 Median :19.23 Median :11.595
## Mean :11.416 Mean :16.125 Mean :19.16 Mean :11.532
## 3rd Qu.:12.094 3rd Qu.:17.058 3rd Qu.:20.24 3rd Qu.:12.643
## Max. :15.412 Max. :22.479 Max. :25.64 Max. :17.663
## measurement_13 measurement_14 measurement_15 measurement_16
## Min. :10.89 Min. : 9.14 Min. : 9.104 Min. : 9.701
## 1st Qu.:14.95 1st Qu.:15.07 1st Qu.:14.037 1st Qu.:15.199
## Median :15.72 Median :16.13 Median :15.057 Median :16.323
## Mean :15.78 Mean :16.09 Mean :15.042 Mean :16.311
## 3rd Qu.:16.54 3rd Qu.:17.13 3rd Qu.:16.014 3rd Qu.:17.514
## Max. :22.71 Max. :22.30 Max. :21.626 Max. :24.094
## measurement_17
## Min. : 196.8
## 1st Qu.: 628.3
## Median : 715.1
## Mean : 734.1
## 3rd Qu.: 813.4
## Max. :1312.8str(product_failure_complete)## 'data.frame': 26570 obs. of 25 variables:
## $ failure : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 2 1 1 ...
## $ product_code : Factor w/ 5 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ loading : num 80.1 84.9 82.4 101.1 188.1 ...
## $ attribute_0 : Factor w/ 2 levels "material_7","material_5": 1 1 1 1 1 1 1 1 1 1 ...
## $ attribute_1 : Factor w/ 3 levels "material_8","material_5",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ attribute_2 : num 9 9 9 9 9 9 9 9 9 9 ...
## $ attribute_3 : num 5 5 5 5 5 5 5 5 5 5 ...
## $ measurement_0 : num 7 14 12 13 9 11 12 4 9 10 ...
## $ measurement_1 : num 8 3 1 2 2 4 2 8 6 4 ...
## $ measurement_2 : num 4 3 5 6 8 0 4 8 5 7 ...
## $ measurement_3 : num 18 18.2 18.1 17.3 19.3 ...
## $ measurement_4 : num 12.5 11.5 11.7 11.2 12.9 ...
## $ measurement_5 : num 15.7 17.7 16.7 18.6 17 ...
## $ measurement_6 : num 19.3 17.9 18.2 18.3 15.7 ...
## $ measurement_7 : num 11.7 12.7 12.7 12.6 11.3 ...
## $ measurement_8 : num 20.2 17.9 18.3 19.1 18.1 ...
## $ measurement_9 : num 10.7 12.4 12.7 12.5 10.3 ...
## $ measurement_10: num 15.9 17.9 15.6 16.3 17.1 ...
## $ measurement_11: num 17.6 17.9 16.2 18.4 19.9 ...
## $ measurement_12: num 15.2 11.8 13.8 10 12.4 ...
## $ measurement_13: num 15 14.7 16.7 15.2 16.2 ...
## $ measurement_14: num 14.7 15.4 18.6 15.6 12.8 ...
## $ measurement_15: num 13 14.4 14.1 16.2 13.2 ...
## $ measurement_16: num 14.7 15.6 17.9 17.2 16.4 ...
## $ measurement_17: num 764 682 663 826 580 ...skim(product_failure_complete)| Name | product_failure_complete |
| Number of rows | 26570 |
| Number of columns | 25 |
| _______________________ | |
| Column type frequency: | |
| factor | 4 |
| numeric | 21 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| failure | 0 | 1 | FALSE | 2 | No: 20921, Yes: 5649 |
| product_code | 0 | 1 | FALSE | 5 | C: 5765, E: 5343, B: 5250, D: 5112 |
| attribute_0 | 0 | 1 | FALSE | 2 | mat: 21320, mat: 5250 |
| attribute_1 | 0 | 1 | FALSE | 3 | mat: 10865, mat: 10362, mat: 5343 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| loading | 0 | 1 | 128.01 | 39.20 | 33.16 | 100.02 | 122.46 | 149.32 | 385.86 | ▃▇▂▁▁ |
| attribute_2 | 0 | 1 | 6.75 | 1.47 | 5.00 | 6.00 | 6.00 | 8.00 | 9.00 | ▅▇▁▅▃ |
| attribute_3 | 0 | 1 | 7.24 | 1.46 | 5.00 | 6.00 | 8.00 | 8.00 | 9.00 | ▃▃▁▇▃ |
| measurement_0 | 0 | 1 | 7.42 | 4.12 | 0.00 | 4.00 | 7.00 | 10.00 | 29.00 | ▆▇▂▁▁ |
| measurement_1 | 0 | 1 | 8.23 | 4.20 | 0.00 | 5.00 | 8.00 | 11.00 | 29.00 | ▅▇▃▁▁ |
| measurement_2 | 0 | 1 | 6.26 | 3.31 | 0.00 | 4.00 | 6.00 | 8.00 | 24.00 | ▅▇▂▁▁ |
| measurement_3 | 0 | 1 | 17.79 | 1.00 | 13.97 | 17.11 | 17.78 | 18.46 | 21.50 | ▁▃▇▃▁ |
| measurement_4 | 0 | 1 | 11.73 | 1.00 | 8.01 | 11.05 | 11.73 | 12.41 | 16.48 | ▁▅▇▁▁ |
| measurement_5 | 0 | 1 | 17.09 | 1.01 | 12.07 | 16.39 | 17.10 | 17.78 | 21.42 | ▁▂▇▃▁ |
| measurement_6 | 0 | 1 | 17.54 | 1.02 | 12.71 | 16.86 | 17.52 | 18.19 | 21.54 | ▁▁▇▅▁ |
| measurement_7 | 0 | 1 | 11.64 | 1.08 | 7.97 | 10.98 | 11.69 | 12.36 | 15.42 | ▁▃▇▃▁ |
| measurement_8 | 0 | 1 | 18.96 | 1.05 | 15.22 | 18.27 | 18.97 | 19.68 | 23.81 | ▁▆▇▂▁ |
| measurement_9 | 0 | 1 | 11.42 | 1.00 | 7.54 | 10.74 | 11.45 | 12.09 | 15.41 | ▁▃▇▃▁ |
| measurement_10 | 0 | 1 | 16.13 | 1.40 | 9.32 | 15.22 | 16.18 | 17.06 | 22.48 | ▁▂▇▂▁ |
| measurement_11 | 0 | 1 | 19.16 | 1.58 | 12.46 | 18.12 | 19.23 | 20.24 | 25.64 | ▁▂▇▃▁ |
| measurement_12 | 0 | 1 | 11.53 | 1.65 | 5.17 | 10.53 | 11.60 | 12.64 | 17.66 | ▁▂▇▃▁ |
| measurement_13 | 0 | 1 | 15.78 | 1.23 | 10.89 | 14.95 | 15.72 | 16.54 | 22.71 | ▁▇▇▁▁ |
| measurement_14 | 0 | 1 | 16.09 | 1.47 | 9.14 | 15.07 | 16.13 | 17.13 | 22.30 | ▁▂▇▃▁ |
| measurement_15 | 0 | 1 | 15.04 | 1.51 | 9.10 | 14.04 | 15.06 | 16.01 | 21.63 | ▁▃▇▂▁ |
| measurement_16 | 0 | 1 | 16.31 | 1.79 | 9.70 | 15.20 | 16.32 | 17.51 | 24.09 | ▁▃▇▂▁ |
| measurement_17 | 0 | 1 | 734.15 | 163.43 | 196.79 | 628.26 | 715.09 | 813.43 | 1312.79 | ▁▅▇▂▁ |
productfailure1 = product_failure_complete %>% dplyr::select(c("failure","loading","attribute_0","attribute_1","attribute_2","attribute_3"))
productfailure2 = product_failure_complete %>% dplyr::select(c("failure","measurement_0","measurement_1","measurement_2","measurement_3","measurement_4","measurement_5"))
productfailure3 = product_failure_complete %>% dplyr::select(c("failure","measurement_6","measurement_7","measurement_8","measurement_9","measurement_10","measurement_11"))
productfailure4 = product_failure_complete %>% dplyr::select(c("failure","measurement_12","measurement_13","measurement_14","measurement_15","measurement_16","measurement_17"))
summary(productfailure1)## failure loading attribute_0 attribute_1
## No :20921 Min. : 33.16 material_7:21320 material_8:10865
## Yes: 5649 1st Qu.:100.02 material_5: 5250 material_5:10362
## Median :122.46 material_6: 5343
## Mean :128.01
## 3rd Qu.:149.32
## Max. :385.86
## attribute_2 attribute_3
## Min. :5.000 Min. :5.00
## 1st Qu.:6.000 1st Qu.:6.00
## Median :6.000 Median :8.00
## Mean :6.754 Mean :7.24
## 3rd Qu.:8.000 3rd Qu.:8.00
## Max. :9.000 Max. :9.00ggpairs(productfailure1)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(productfailure2)## failure measurement_0 measurement_1 measurement_2 measurement_3
## No :20921 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :13.97
## Yes: 5649 1st Qu.: 4.000 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.:17.11
## Median : 7.000 Median : 8.000 Median : 6.000 Median :17.78
## Mean : 7.416 Mean : 8.233 Mean : 6.257 Mean :17.79
## 3rd Qu.:10.000 3rd Qu.:11.000 3rd Qu.: 8.000 3rd Qu.:18.46
## Max. :29.000 Max. :29.000 Max. :24.000 Max. :21.50
## measurement_4 measurement_5
## Min. : 8.008 Min. :12.07
## 1st Qu.:11.054 1st Qu.:16.39
## Median :11.735 Median :17.10
## Mean :11.734 Mean :17.09
## 3rd Qu.:12.411 3rd Qu.:17.79
## Max. :16.484 Max. :21.43ggpairs(productfailure2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(productfailure3)## failure measurement_6 measurement_7 measurement_8 measurement_9
## No :20921 Min. :12.71 Min. : 7.968 Min. :15.22 Min. : 7.537
## Yes: 5649 1st Qu.:16.86 1st Qu.:10.978 1st Qu.:18.27 1st Qu.:10.738
## Median :17.52 Median :11.686 Median :18.97 Median :11.445
## Mean :17.54 Mean :11.643 Mean :18.96 Mean :11.416
## 3rd Qu.:18.19 3rd Qu.:12.364 3rd Qu.:19.68 3rd Qu.:12.094
## Max. :21.54 Max. :15.419 Max. :23.81 Max. :15.412
## measurement_10 measurement_11
## Min. : 9.323 Min. :12.46
## 1st Qu.:15.219 1st Qu.:18.12
## Median :16.182 Median :19.23
## Mean :16.125 Mean :19.16
## 3rd Qu.:17.058 3rd Qu.:20.24
## Max. :22.479 Max. :25.64ggpairs(productfailure3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(productfailure4)## failure measurement_12 measurement_13 measurement_14 measurement_15
## No :20921 Min. : 5.167 Min. :10.89 Min. : 9.14 Min. : 9.104
## Yes: 5649 1st Qu.:10.534 1st Qu.:14.95 1st Qu.:15.07 1st Qu.:14.037
## Median :11.595 Median :15.72 Median :16.13 Median :15.057
## Mean :11.532 Mean :15.78 Mean :16.09 Mean :15.042
## 3rd Qu.:12.643 3rd Qu.:16.54 3rd Qu.:17.13 3rd Qu.:16.014
## Max. :17.663 Max. :22.71 Max. :22.30 Max. :21.626
## measurement_16 measurement_17
## Min. : 9.701 Min. : 196.8
## 1st Qu.:15.199 1st Qu.: 628.3
## Median :16.323 Median : 715.1
## Mean :16.311 Mean : 734.1
## 3rd Qu.:17.514 3rd Qu.: 813.4
## Max. :24.094 Max. :1312.8ggpairs(productfailure4)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggcorr(productfailure1, label = "true", label_round = 2)## Warning in ggcorr(productfailure1, label = "true", label_round = 2): data in
## column(s) 'failure', 'attribute_0', 'attribute_1' are not numeric and were
## ignored
ggcorr(productfailure2, label = "true", label_round = 2)## Warning in ggcorr(productfailure2, label = "true", label_round = 2): data in
## column(s) 'failure' are not numeric and were ignored
ggcorr(productfailure3, label = "true", label_round = 2)## Warning in ggcorr(productfailure3, label = "true", label_round = 2): data in
## column(s) 'failure' are not numeric and were ignored
ggcorr(productfailure4, label = "true", label_round = 2)## Warning in ggcorr(productfailure4, label = "true", label_round = 2): data in
## column(s) 'failure' are not numeric and were ignored
ggcorr(product_failure_complete, label = "true", label_round = 2)## Warning in ggcorr(product_failure_complete, label = "true", label_round = 2):
## data in column(s) 'failure', 'product_code', 'attribute_0', 'attribute_1' are
## not numeric and were ignored
ggplot(product_failure_complete, aes(x=loading, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$product_code) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## A B C D E
## No 0.7727451 0.7996190 0.7883781 0.7824726 0.7930002
## Yes 0.2272549 0.2003810 0.2116219 0.2175274 0.2069998 ggplot(product_failure_complete, aes(x=product_code, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$product_code) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## A B C D E
## No 0.7727451 0.7996190 0.7883781 0.7824726 0.7930002
## Yes 0.2272549 0.2003810 0.2116219 0.2175274 0.2069998ggplot(product_failure_complete, aes(x=attribute_2, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$attribute_2) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## 5 6 8 9
## No 0.7883781 0.7878527 0.7996190 0.7727451
## Yes 0.2116219 0.2121473 0.2003810 0.2272549ggplot(product_failure_complete, aes(x=attribute_0, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$attribute_0) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## material_7 material_5
## No 0.7843809 0.7996190
## Yes 0.2156191 0.2003810ggplot(product_failure_complete, aes(x=attribute_1, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$attribute_1) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## material_8 material_5 material_6
## No 0.7810400 0.7911600 0.7930002
## Yes 0.2189600 0.2088400 0.2069998ggplot(product_failure_complete, aes(x=attribute_3, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$attribute_3) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## 5 6 8 9
## No 0.7727451 0.7824726 0.7937358 0.7930002
## Yes 0.2272549 0.2175274 0.2062642 0.2069998ggplot(product_failure_complete, aes(x=measurement_0, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$measurement_0) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## 0 1 2 3 4 5 6
## No 0.8159851 0.7834793 0.7800797 0.8018610 0.7864035 0.7876209 0.7819602
## Yes 0.1840149 0.2165207 0.2199203 0.1981390 0.2135965 0.2123791 0.2180398
##
## 7 8 9 10 11 12 13
## No 0.7844796 0.7928368 0.8037699 0.7884058 0.7682495 0.7877095 0.7890724
## Yes 0.2155204 0.2071632 0.1962301 0.2115942 0.2317505 0.2122905 0.2109276
##
## 14 15 16 17 18 19 20
## No 0.7839506 0.8004386 0.7520891 0.7423581 0.7839196 0.6991150 0.7848101
## Yes 0.2160494 0.1995614 0.2479109 0.2576419 0.2160804 0.3008850 0.2151899
##
## 21 22 23 24 25 26 27
## No 0.8113208 0.8684211 0.7647059 0.7083333 0.5714286 1.0000000 1.0000000
## Yes 0.1886792 0.1315789 0.2352941 0.2916667 0.4285714 0.0000000 0.0000000
##
## 29
## No 1.0000000
## Yes 0.0000000ggplot(product_failure_complete, aes(x=measurement_1, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$measurement_1) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## 0 1 2 3 4 5 6
## No 0.7671958 0.7411945 0.8031496 0.7930781 0.7766821 0.7864955 0.7827368
## Yes 0.2328042 0.2588055 0.1968504 0.2069219 0.2233179 0.2135045 0.2172632
##
## 7 8 9 10 11 12 13
## No 0.7825235 0.7893724 0.7899873 0.7793202 0.7973046 0.8000000 0.8057296
## Yes 0.2174765 0.2106276 0.2100127 0.2206798 0.2026954 0.2000000 0.1942704
##
## 14 15 16 17 18 19 20
## No 0.7892157 0.7927786 0.8004246 0.7634069 0.8018868 0.7810651 0.7659574
## Yes 0.2107843 0.2072214 0.1995754 0.2365931 0.1981132 0.2189349 0.2340426
##
## 21 22 23 24 25 26 27
## No 0.7692308 0.8750000 0.8235294 0.7333333 0.8750000 1.0000000 0.5000000
## Yes 0.2307692 0.1250000 0.1764706 0.2666667 0.1250000 0.0000000 0.5000000
##
## 28 29
## No 1.0000000 0.0000000
## Yes 0.0000000 1.0000000ggplot(product_failure_complete, aes(x=measurement_2, fill = failure)) + geom_bar(position="fill") + theme_bw()
t1 = table(product_failure_complete$failure, product_failure_complete$measurement_2) #create a table object
prop.table(t1, margin = 2 ) #crosstab with proportions##
## 0 1 2 3 4 5 6
## No 0.7799043 0.7973856 0.8044597 0.7808511 0.7871846 0.7960506 0.7984152
## Yes 0.2200957 0.2026144 0.1955403 0.2191489 0.2128154 0.2039494 0.2015848
##
## 7 8 9 10 11 12 13
## No 0.7752420 0.7793944 0.7856000 0.7972696 0.8030457 0.7717087 0.7522124
## Yes 0.2247580 0.2206056 0.2144000 0.2027304 0.1969543 0.2282913 0.2477876
##
## 14 15 16 17 18 19 20
## No 0.7713178 0.7414966 0.7472527 0.7014925 0.7567568 0.6842105 0.6250000
## Yes 0.2286822 0.2585034 0.2527473 0.2985075 0.2432432 0.3157895 0.3750000
##
## 21 22 23 24
## No 0.6250000 0.0000000 1.0000000 0.5000000
## Yes 0.3750000 1.0000000 0.0000000 0.5000000ggplot(product_failure_complete, aes(x=measurement_3, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_4, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_5, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_6, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_7, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_8, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_9, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_10, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_11, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_12, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_13, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_14, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_15, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_16, fill = failure)) + geom_bar(position="fill") + theme_bw()
ggplot(product_failure_complete, aes(x=measurement_17, fill = failure)) + geom_bar(position="fill") + theme_bw()